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local Sum, parent = torch.class('nn.Sum', 'nn.Module')
function Sum:__init(dimension, nInputDims, sizeAverage, squeeze)
parent.__init(self)
self.dimension = dimension or 1
-- do not assign default value to nInputDims or it will break backward compatibility
self.nInputDims = nInputDims
self.sizeAverage = sizeAverage or false
if squeeze ~= nil then
assert(type(squeeze) == 'boolean', 'squeeze has to be true/false')
self.squeeze = squeeze
else
self.squeeze = true
end
end
function Sum:_getPositiveDimension(input)
local dimension = self.dimension
if dimension < 0 then
dimension = input:dim() + dimension + 1
elseif self.nInputDims and input:dim()==(self.nInputDims+1) then
dimension = dimension + 1
end
assert(input:dim() >= dimension, "dimension exceeds input dimensions")
return dimension
end
function Sum:updateOutput(input)
local dimension = self:_getPositiveDimension(input)
if type(self.output) == 'number' then
self.output = input.new()
end
self.output:sum(input, dimension)
if self.sizeAverage then
self.output:div(input:size(dimension))
end
if (self.squeeze == nil or self.squeeze) and self.output:nDimension() > 1 then
self.output:set(self.output:select(dimension, 1))
end
return self.output
end
function Sum:updateGradInput(input, gradOutput)
local dimension = self:_getPositiveDimension(input)
-- zero-strides don't work with MKL/BLAS, so
-- don't set self.gradInput to zero-stride tensor.
-- Instead, do a deepcopy
local size = input:size()
size[dimension] = 1
if not gradOutput:isContiguous() then
self._gradOutput = self._gradOutput or gradOutput.new()
self._gradOutput:resizeAs(gradOutput):copy(gradOutput)
gradOutput = self._gradOutput
end
gradOutput = gradOutput:view(size)
self.gradInput:resizeAs(input)
self.gradInput:copy(gradOutput:expandAs(input))
if self.sizeAverage then
self.gradInput:div(input:size(dimension))
end
return self.gradInput
end
function Sum:clearState()
nn.utils.clear(self, '_gradOutput')
return parent.clearState(self)
end
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